COVID-19 Fake News Detection using Pre-Tuned BERT-Based Transfer Learning Models

The World Health Organization declared the Coronavirus disease in 2019; that was a very hard time for people, and every single day, a new crisis emerged. In that case, everyone shares their stories on the social media platform on a daily basis, but no one is sure that information is true or misleading, and that becomes a challenge to detect differences between them. And to tackle this problem, this paper has explored the veracity of social media stories using some machine learning models. The goal of this paper is to test three different BERT base pre-trained transform learning models (BERT, DistilBERT, and RoBERTa) on an English COVID-19 fake news dataset to detect the fake and true news separately. We explore their capability with precision, recall, and F1-score to achieve better results compared with the previous research.